{"type":"ScholarlyArticle","title":"Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders","authors":[{"name":"Xiaopeng Yang","slug":"xiaopeng-yang","given":"Xiaopeng","family":"Yang","affiliation":"School of Computer Science ,; Faculty of Mathematics , University of Waterloo Waterloo , ON , Canada N2L 3G1"},{"name":"Xiaowen Lin","slug":"xiaowen-lin","given":"Xiaowen","family":"Lin","affiliation":"School of Computer Science ,; Faculty of Mathematics , University of Waterloo Waterloo , ON , Canada N2L 3G1"},{"name":"Shunda Suo","slug":"shunda-suo","given":"Shunda","family":"Suo","affiliation":"School of Computer Science ,; Faculty of Mathematics , University of Waterloo Waterloo , ON , Canada N2L 3G1"},{"name":"Ming Li","slug":"ming-li","given":"Ming","family":"Li","affiliation":"School of Computer Science ,; Faculty of Mathematics , University of Waterloo Waterloo , ON , Canada N2L 3G1"}],"abstract":"Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5).","doi":"10.24963/ijcai.2018/631","url":"https://ijcai.org/proceedings/2018/631","pdf":"https://ijcai.org/proceedings/2018/0631.pdf","markdown":"https://ijcai.org/proceedings/2018/631.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/631","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":631,"pages":{"first":4539,"last":4545},"keywords":["Natural Language Processing: Natural Language Generation","Machine Learning: Neural Networks","Machine Learning: Machine Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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